---
title: "How Does The Type of Jump Block Performed Affect Landing Kinetics in Female Volleyball Athletes?"
output: 
  flexdashboard::flex_dashboard:
    vertical_layout: fill
    source_code: embed
    logo: "~/Desktop/MTH209/Labs/UDlogo.png"
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(signal)
library(DT)
library(reshape2)

# Create filter
order <- 4
cutoff_freq <- 1
butterworth_filter <- butter(order, cutoff_freq, type="low")

# CMJ Example
CMJ_Ex <- read_csv("~/Desktop/MTH209/Labs/CMJ_Example.csv")

# Take abs value, change neg force to pos
CMJ_Ex$LFz <- abs(CMJ_Ex$LFz)
CMJ_Ex$RFz <- abs(CMJ_Ex$RFz)

# Create total force variable
CMJ_Ex <- CMJ_Ex %>% mutate(Fz = LFz+RFz)

# Convert N to BWs
CMJ_Ex$LFz <- (CMJ_Ex$LFz / 790)
CMJ_Ex$RFz <- (CMJ_Ex$RFz / 790)
CMJ_Ex$Fz <- (CMJ_Ex$Fz / 790)

# Filter data
CMJ_Ex$LFz <- filter(butterworth_filter, CMJ_Ex$LFz)
CMJ_Ex$RFz <- filter(butterworth_filter, CMJ_Ex$RFz)
CMJ_Ex$Fz <- filter(butterworth_filter, CMJ_Ex$Fz)

# Read CSV files containing Force Plate Data
LS03FP <- read_csv("~/Desktop/MTH209/Labs/LS03FP.csv")
LS05FP <- read_csv("~/Desktop/MTH209/Labs/LS05FP.csv")
LS06FP <- read_csv("~/Desktop/MTH209/Labs/LS06FP.csv")
LS07FP <- read_csv("~/Desktop/MTH209/Labs/LS07FP.csv")
LS08FP <- read_csv("~/Desktop/MTH209/Labs/LS08FP.csv")
LS09FP <- read_csv("~/Desktop/MTH209/Labs/LS09FP.csv")
LS10FP <- read_csv("~/Desktop/MTH209/Labs/LS10FP.csv")
LS11FP <- read_csv("~/Desktop/MTH209/Labs/LS11FP.csv")
LS12FP <- read_csv("~/Desktop/MTH209/Labs/LS12FP.csv")
LS13FP <- read_csv("~/Desktop/MTH209/Labs/LS13FP.csv")
LS14FP <- read_csv("~/Desktop/MTH209/Labs/LS14FP.csv")
LS15FP <- read_csv("~/Desktop/MTH209/Labs/LS15FP.csv")
LS16FP <- read_csv("~/Desktop/MTH209/Labs/LS16FP.csv")

LW03FP <- read_csv("~/Desktop/MTH209/Labs/LW03FP.csv")
LW04FP <- read_csv("~/Desktop/MTH209/Labs/LW04FP.csv")
LW05FP <- read_csv("~/Desktop/MTH209/Labs/LW05FP.csv")
LW06FP <- read_csv("~/Desktop/MTH209/Labs/LW06FP.csv")
LW07FP <- read_csv("~/Desktop/MTH209/Labs/LW07FP.csv")
LW08FP <- read_csv("~/Desktop/MTH209/Labs/LW08FP.csv")
LW09FP <- read_csv("~/Desktop/MTH209/Labs/LW09FP.csv")
LW10FP <- read_csv("~/Desktop/MTH209/Labs/LW10FP.csv")
LW11FP <- read_csv("~/Desktop/MTH209/Labs/LW11FP.csv")
LW12FP <- read_csv("~/Desktop/MTH209/Labs/LW12FP.csv")
LW13FP <- read_csv("~/Desktop/MTH209/Labs/LW13FP.csv")
LW14FP <- read_csv("~/Desktop/MTH209/Labs/LW14FP.csv")
LW15FP <- read_csv("~/Desktop/MTH209/Labs/LW15FP.csv")
LW16FP <- read_csv("~/Desktop/MTH209/Labs/LW16FP.csv")


# Create IDs for each row, correspond to research documentation linking FP, 
# Block Type, Subject, etc.
ID <- c("LS03", "LS05", "LS06", "LS07", "LS08", "LS09", "LS10", "LS11",
        "LS12", "LS13", "LS14", "LS15", "LS16", "LW03", "LW04", "LW05", "LW06", 
        "LW07", "LW08", "LW09", "LW10", "LW11", "LW12", "LW13", "LW14", "LW15", 
        "LW16")

# Assign block types. S = Straight, T = Tilt
#BlockType <- c("S", "S", "S", "S", "T", "T", "T", "T", "S", "T", "S", "T",
#               "S", "S", "T", "S", "S", "T", "S", "T", "S", "S", "T", "T", "S", 
#               "T", "T")

BlockType <- c("Straight", "Straight", "Straight", "Straight", "Tilt", "Tilt", "Tilt", "Tilt", "Straight", "Tilt", "Straight", "Tilt", "Straight", "Straight", "Tilt", "Straight", "Straight", "Tilt", "Straight", "Tilt", "Straight", "Straight", "Tilt", "Tilt", "Straight", "Tilt", "Tilt")

BW <- c(790, 790, 790, 790, 790, 790, 790, 790, 790, 790, 790, 790, 790,
          903, 903, 903, 903, 903, 903, 903, 903, 903, 903, 903, 903, 903,
          903)

# Create a list containing the data frames of FP data
FPDF <- list(LS03FP, LS05FP, LS06FP, LS07FP, LS08FP, LS09FP, LS10FP, LS11FP,
             LS12FP, LS13FP, LS14FP, LS15FP, LS16FP, LW03FP, LW04FP, LW05FP,
             LW06FP, LW07FP, LW08FP, LW09FP, LW10FP, LW11FP, LW12FP, LW13FP,
             LW14FP, LW15FP, LW16FP)

# Assign names to each df of FP data, so that data can be access 
# using FPDF$(name)
names(FPDF) <- c("LS03FP", "LS05FP", "LS06FP", "LS07FP", "LS08FP", "LS09FP",
                 "LS10FP", "LS11FP", "LS12FP", "LS13FP", "LS14FP", "LS15FP",
                 "LS16FP", "LW03FP", "LW04FP", "LW05FP", "LW06FP", "LW07FP",
                 "LW08FP", "LW09FP", "LW10FP", "LW11FP", "LW12FP", "LW13FP",
                 "LW14FP", "LW15FP", "LW16FP")
  
# Loop through each data frame in the list
for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
    
  # Make LFz, RFz variables positive
  df$LFz <- abs(df$LFz)
  df$RFz <- abs(df$RFz)
    
  # Assign the modified data frame back to the list
  FPDF[[i]] <- df
}

# Loop through each data frame in the list
for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
  
  # Create Fz variable
  df <- df %>% mutate(Fz = LFz+RFz)
  
  # Assign the modified data frame back to the list
  FPDF[[i]] <- df
}

# Loop through each data frame in the list
for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
  
  # Filter each FP data using created butterworth filter
  df$LFz <- filter(butterworth_filter, df$LFz)
  df$RFz <- filter(butterworth_filter, df$RFz)
  df$Fz <- filter(butterworth_filter, df$Fz)
  
  # Assign the modified data frame back to the list
  FPDF[[i]] <- df
}

# Peak Impact Force, Depending on block type
PeakGRF <- c()

for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
  
  # Find the peakGRF
  PeakGRF <- c(PeakGRF, max(df$Fz))
}

# Landing Asymmetry, Depending on block type
LandAsym <- c()

for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
  
  # Find the Landing Asymmetry
  LandAsym <- c(LandAsym, (max(df$RFz) - max(df$LFz)))
}

LandAsym_pct <- c()

for (i in seq_along(FPDF)) {
  # Access the i-th data frame
  df <- FPDF[[i]]
  
  # Find the Landing Asymmetry
  LandAsym_pct <- c(LandAsym_pct, 100*(abs(((max(df$RFz) -
                                      max(df$LFz)))/
                      ifelse(max(df$Rfz) > max(df$LFz), max(df$LFz), max(df$RFz)))))
}

# Average Vertical Loading Rate, Depending on block type
AVLR <- c()

for (i in seq_along(FPDF)) {
  df <- FPDF[[i]]
  
  twentypct <- (0.2 * (max(df$Fz)))
  
  eightypct <- (0.8 * (max(df$Fz)))
  
  itwenty <- which(df$Fz >= twentypct)[1]
  
  ieighty <- which(df$Fz >= eightypct)[1]
  
  AVLR <- c(AVLR, ((eightypct-twentypct)/((ieighty-itwenty)/1200)))
}

#Create Final DF

final <- data.frame(ID=ID, BlockType=BlockType, BW=BW, PeakGRF=PeakGRF,
                    LandAsym=LandAsym, LandAsym_pct=LandAsym_pct, AVLR=AVLR)

# Convert N to BWs
final$PeakGRF <- (final$PeakGRF / BW)
final$LandAsym <- (final$LandAsym / BW)
final$AVLR <- (final$AVLR / BW)
```

```{css}
body {
font-size: 15px
}
```

My Project
===

Column {data-width=1}
---

### **Background**

Jumping and landing is essential to the game of volleyball. The impacts associated with these movements can lead to acute injuries and overuse injuries, primarily of the lower extremities. Common injuries include patellar tendon tendinopathy, ACL tears, meniscus tears, stress fractures, and ankle sprains (Young et al., 2023). Examining the forces associated with these movements can provide important insight into the injury risk associated with a certain individual or movement.

Jumping and landing forces are typically quantified as ground reaction forces (GRFs) and are measured by a force plate. A ground reaction force is the force applied to a body by the ground, as a result of an equal and opposite force applied by the body. This is an expression of Newton's Third Law. Essentially, when you push on the ground, the ground pushes back at you.

Greater peak vertical GRFs may be related to an increased risk of lower extremity injury, particularly of the knee. vGRFs load the knee and contribute to knee instability which increases the risk for injury (Hewett et al., 2005; Yu & Garrett, 2007). One prospective study found that those went on to tear their ACL exhibited peak vGRFs 20% greater than those who did not tear their ACL (Hewett et al., 2005). If one block type is associated with a greater peak vGRF, this may indicate a greater injury risk associated with the jump block.

Bilateral vGRF asymmetry is thought to be an injury risk factor. Previous literature suggests that a bilateral asymmetry of 10-15% may increase risk of injury and decrease performance (Hewett et al., 2010; Paterno et al., 2007). If one block type is associated with a greater bilateral vGRF asymmetry, this may indicate a greater injury risk associated with the jump block.

A greater average vertical loading rate (AVLR) upon landing may be associated with a greater injury risk. A greater AVLR means that the body is absorbing greater impact in a shorter time period. This means that the force absorbed is greater, and the time that the body has to respond and adapt is shorter -- potentially increasing the risk of injury (Bates et al., 2013; Kabacinski et al., 2017). If one block type exhibits a greater AVLR, this jump block type may be associated with a greater injury risk.

Column {data-width=1}
---

### **Methods**

The data used in this project was provided by Dr. Matthew Beerse, and obtained through a previous research project. In this previous project, NCAA Division 1 female volleyball athletes were recruited from the University of Dayton. These athletes completed a series of fourteen jump blocks. Seven jump blocks of each block type (straight or tilt) were performed. All tilt blocks were performed to the same side. The order of block type was randomized, and the athletes were cued by a video feed of an attacking player spiking the ball towards them. The athletes did not block a moving ball, they acted as if they were blocking a ball at a height slightly above a regulation volleyball net. Each jump block began with one foot on each force plate, and for the trial to be considered valid, the athlete had to land with one foot on each plate as well. Three-dimensional motion capture data was collected as well; however, only kinetic (force) data will be analyzed in this project.

At the core of this project is the idea of injury prevention. I selected dependent variables that are related to injury prevention, as is discussed in the background section.

Twelve participants were included in the previous research project. In this project, only two participants were included. This may initially seem like a quite small sample; however, these two subject provide a total of 27 jump trials. This will be sufficient to develop preliminary impressions of larger trends.

### **Research Questions**

In this project, I aim to answer the following questions about the kinetics of jump block landings:

1.  How does the type of block performed affect **peak total vertical ground reaction force** upon landing?

2.  How does the type of block performed affect **vertical ground reaction force asymmetry** at the time of peak total vertical ground reaction force?

3.  How does the type of block performed affect **average vertical loading rate** upon landing?

A Jump Block
===

Column {data-width=1}
---

### **What is a Jump Block?**

A **jump block** is a common volleyball technique in which a defensive player jump up to the net and attempts to block the ball from crossing over the net. When an attacking player attempts to spike the ball, a defensive player will commonly attempt a jump block.

There are two types of jump blocks performed in this study.

A **straight block** is a jump block in which the defensive player jumps straight up and attempts to block a ball directly in front of them.

A **tilt block** is a jump block in which the defensive player jumps straight up, but has to lean to one side or another, in order to attempt to make contact with the ball.

Column {data-width=1}
---

### 

```{r, fig.cap="**Left:** Tilt; **Right:** Straight"}
knitr::include_graphics("~/Desktop/MTH209/Labs/VolleyballBlock.jpeg", error=F)
```

A Countermovement Jump
===

Column {data-width=1}
---

### **What is a Countermovement Jump?**

The term countermovement jump refers to a typical standing vertical jump. A countermovement jump consists of a series of distinct, continuous phases.

**Standing** - The athlete begins the jump in a static standing position.

**Unweighting** - The athlete begins to move downwards into a squatted position. This is the "countermovement" that gives the jump its name.

**Braking** - During the braking phase, the athlete begins to resist their descent that began during the unweighting phase. This is when the athlete first begins to apply force greater than their body weight. The athlete begins accelerating vertically during this phase.

**Propulsion** - During the propulsion phase, the athlete continues to apply a larger vertical force, but their body now has a positive vertical velocity, meaning they are now moving up.

**Flight** - During this phase, the athlete is in the air.

**Landing** - This phase begins at the moment of initial contact and continues until the athlete is once again in a static standing position.

Column {data-width=1}
---

### **Vertical Ground Reaction Force During CMJ**

```{r}
# Plot CMJ
CMJ_Ex %>% ggplot(aes(x=Frame, y=Fz)) + geom_line(color="#CE1141", size=1) +
  geom_text(aes(label="Unweighting"), x=110, y=1.25, size=3.5) +
  geom_text(aes(label="Braking"), x=240, y=2.75, size=3.5) +
  geom_text(aes(label="Propulsize"), x=525, y=2.75, size=3.5) +
  geom_text(aes(label="Flight"), x=750, y=0.2, size=3.5) +
  geom_text(aes(label="Landing"), x=1150, y=1.5, size=3.5) +
  geom_segment(aes(x=250, y=-0.1, xend=250, yend=2), color="black", size=0.5) +
  geom_segment(aes(x=375, y=2, xend=375, yend=3.5), color="black", size=0.5) +
  geom_segment(aes(x=510, y=-0.1, xend=510, yend=2), color="black", size=0.5) +
  geom_segment(aes(x=1000, y=-0.1, xend=1000, yend=2), color="black", size=0.5) +
  labs(x="Frame", y="Total Ground Reaction Force (BW)",
       title="Example of Typical Countermovement Jump Force Profile") +
  theme_minimal()
```

My Data
===

Column {.tabset data-width=650}
---

### **Complete Table**

```{r}
datatable(final) %>% formatRound("PeakGRF", digits=2) %>%
  formatRound("LandAsym", digits=2) %>%
  formatRound("AVLR", digits=2) %>%
  formatRound("LandAsym_pct", digits=2)
```

### **Force Plate Data Frame**

```{r}
datatable(FPDF$LS03FP) %>% formatRound("LFz", digits=2) %>%
  formatRound("RFz", digits=2) %>% formatRound("Fz", digits=2)
```

Column {.tabset data-width=350}
---

### **Table Variables**

**ID:** The ID corresponds to a specific participant and trial.

**BlockType:** Identifies the type of block performed.

**BW:** Body weight, in newtons.

**PeakGRF:** The maximum total ground reaction force during landing, in bodyweights.

**LandAsym:** The difference between peak vertical ground reaction forces through the left and right feet, in bodyweights. A negative value indicates that the left side is favored, while a positive value indicates that the right side is favored.

**LandAsym_pct:** LandAsym represented as an absolute percentage.

**AVLR:** The average vertical loading rate from 20% of impact peak to 80% of impact peak, in bodyweights per second. This is determined by dividing the peak total ground reaction force by the time it takes to reach the peak total ground reaction force.



The previous three variables (PeakGRF, LandAsym, and AVLR) are calculated using a force plate data frame corresponding to each jump block trial. An example of a force plate data frame is shown in the next tab.

### **Force Plate Variables**

**Frame:** The frame number. Frame number 1 occurs at the moment of initial contact.

**LFz:** The vertical ground reaction force for the left foot, in newtons.

**RFz:** The vertical ground reaction force for the right foot, in newtons.

**Fz:** The total vertical ground reaction force, in newtons. Calculated by combining the LFz and RFz variables.

Before being stored in the final data frame, each variable is filtered with a fourth order, low pass Butterworth filter.

Peak Force
===

Column {data-width=1}
---

### **Peak**

```{r}
final %>% ggplot(aes(x=BlockType, y=PeakGRF, fill=BlockType)) +
  geom_boxplot(color=c("black")) +
  labs(x="Block Type", y="Peak Vertical Ground Reaction Force (BW)",
       title="Peak Vertical Ground Reaction Force by Block Type", fill="Block Type") +
  scale_fill_manual(values = c("Straight"="#004B8D", "Tilt"="#CE1141")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5)) +
  ylim(2.5, 5.5)
```

### **Tilt**

```{r}
FPDF$LS08FP %>% 
  melt(id.vars="Frame", variable.name="Variable", value.name="GRF") %>% 
  ggplot(aes(x=Frame, y=GRF, color=Variable)) + geom_line(size=1) +
  labs(x="Frame", y="Vertical Ground Reaction Forces (N)", 
       title="Left, Right, and Total vGRFs for T Landing") +
  scale_color_manual(values=c("LFz"="#CE1141", "RFz"="#004B8D", "Fz"="black")) +
  theme_minimal()
```

Column {data-width=1}
---

### **Analysis**

Peak total vertical ground reaction force is generally greater and more variable when straight blocks are performed. Straight blocks had a greater median peak vertical ground reaction force. Tilt blocks had a much smaller spread of peak vertical ground reaction forces.

Initially, it may appear that straight block landings are more "forceful"; however, this is not necessarily the case. Tilt blocks tend to create two maxima of total force. This is because the left and right feet impact the force plate at slightly different times. Straight blocks tend to lead to both feet impacts the ground at a more similar time. When both feet land at a similar time, the vGRF peaks overlap and **sum** to form a larger peak. An example of this is shown directly below. When there is a slight time delay between foot strikes, the vGRF peaks do **not sum**. Instead, they create two maxima. An example of this is shown in bottom-left.

For this reason, it is possible that the lesser median peak vertical ground reaction force of the tilt block does not reflect a more forceful landing.

### **Straight**

```{r}
FPDF$LS03FP %>% 
  melt(id.vars="Frame", variable.name="Variable", value.name="GRF") %>% 
  ggplot(aes(x=Frame, y=GRF, color=Variable)) + geom_line(size=1) +
  labs(x="Frame", y="Vertical Ground Reaction Forces (N)", 
       title="Left, Right, and Total vGRFs for S Landing") +
  scale_color_manual(values=c("LFz"="#CE1141", "RFz"="#004B8D", "Fz"="black")) +
  theme_minimal()
```

L/R Asymmetry
===

Column {data-width=1}
---

###

```{r}
final %>% ggplot(aes(x=BlockType, y=LandAsym, fill=BlockType)) +
  geom_boxplot(color="black") +
  geom_text(aes(label="Favors Left Side", x="Tilt", y=-2), vjust=-8,
            size=4, color="black") +
  geom_text(aes(label="Favors Right Side", x="Tilt", y=2), vjust=-8,
            size=4, color="black") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  labs(x="Block Type", y="Landing Asymmetry (BW)",
       title="Landing Asymmetry by Block Type", fill="Block Type") +
  scale_fill_manual(values = c("Straight"="#004B8D", "Tilt"="#CE1141")) +
  coord_flip() +
  ylim(-3, 3) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))
```

Column {data-width=1}
---

### **Analysis**

On the left, landing asymmetry is presenting in bodyweights. Values to the left of the dotted dividing line at x=0 indicate that the left foot absorbed a greater force upon landing, while values to the right of the dotted line indicate that the right foot absorbed a greater force upon landing.

Based on this boxplot, straight blocks are relatively symmetrical on average, with the median very near the dotted line. There is a fairly large spread in straight block symmetry, however. Tilt blocks clearly favor the left side, with a median lower than -1 BW and nearly all values below zero. The spread of tilt blocks is smaller than the spread of straight blocks.

Below, landing asymmetry is presented as a percentage of peak vGRF. Once again, tilt blocks show a greater asymmetry than straight blocks. Interestingly, both tilt blocks and straight blocks show an average asymmetry far greater than the threshold of 15% (dotted horizontal line), identified in previous literature as being a threshold for injury risk.

### **Asymmetry as a %**

```{r}
final_summary <- final %>%
  group_by(BlockType) %>%
  summarize(avg_LandAsym_pct = mean(LandAsym_pct))

# Plot bar chart
ggplot(final_summary, aes(x = BlockType, y = avg_LandAsym_pct, fill = BlockType)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) + 
  geom_hline(yintercept = 15, linetype = "dashed", color = "black", size=2) +
  labs(x = "Block Type", y = "Average Landing Asymmetry (%)", 
       title = "Average Landing Asymmetry by Block Type",
       fill = "Block Type") +
  scale_fill_manual(values = c("Straight" = "#004B8D", "Tilt" = "#CE1141")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))
```

Vertical Loading Rate
===

Column {data-width=1}
---

###

```{r}
final_summary_avlr <- final %>%
  group_by(BlockType) %>%
  summarize(avg_AVLR = mean(AVLR),
            sd_AVLR = sd(AVLR)) 

ggplot(final_summary_avlr, aes(x = BlockType, y = avg_AVLR, fill = BlockType)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) + 
  geom_errorbar(aes(ymin = avg_AVLR - sd_AVLR, ymax = avg_AVLR + sd_AVLR),
                position = position_dodge(width = 0.8), width = 0.25) + 
  labs(x = "Block Type", y = "Average Vertical Loading Rate (BW/s)", 
       title = "Average Vertical Loading Rate by Block Type",
       fill = "Block Type") +
  scale_fill_manual(values = c("Straight" = "#004B8D", "Tilt" = "#CE1141")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))
```

Column {data-width=1}
---

### **Analysis**

Generally, straight blocks have a greater AVLR; however, the data is also more spread. Tilt blocks have a lower AVLR, and a smaller standard deviation. The data is much closer together. A greater AVLR indicates that when a straight block is performed, either a greater amount of force is absorbed over the same period, or the same amount of force is absorbed over a shorter period.

To visualize how AVLR is determined, a line plot is included below. AVLR is calculated as the average slope between the first vertical line (at 20% of peak vGRF) and the second vertical line (at 80% of peak vGRF).

###

```{r}
FPDF$LS03FP %>% ggplot(aes(x=Frame, y=Fz)) + geom_line(color="#CE1141", size=1) +
  geom_vline(xintercept=16) + geom_vline(xintercept=75) +
  xlim(0,200) +
  labs(x="Frame", y="Vertical Ground Reaction Force (N)")
```

Discussion
===

Column {data-width=1}
---

### **Discussion**

This investigation provides evidence that coaches, athletes, and clinicians should take the type of block performed into account when considering athlete performance. 

The data suggests that peak vertical GRF is generally greater when a straight block is performed. It is likely that this is not an accurate finding, as the left and right vGRF peaks tend to sum in straight blocks, but they create two maxima in tilt blocks.

Tilt blocks are asymmetrical compared to straight blocks. In an asymmetrical landing, more force is absorbed by one leg than another leg. This could place one leg at risk of injury. It may be worthwhile for clinicians, coaches, and athletes to work towards a more symmetrical landing through technique practice and strengthening. Additionally, it may be possible to limit the volume of tilt blocks performed in a non-competition setting, or even adjust game strategies to limit the number of tilt blocks performed in a game.

The data suggests that the average vertical loading rate is greater in a straight block than a tilt block. It is possible that this is due to the fact that some tilt landings have two maxima in vGRF. Coaches, athletes, and clinicians may be able to develop strategies to limit this loading rate without creating an asymmetrical landing or decreasing performance. At the very least, coaches should be aware of the forces an athlete experiences during a straight block landing.

Overall, coaches, athletes, and clinicians should be aware of the differences between straight blocks and tilt blocks. One is not clearly more risky than the other; however, there are risks associated with each of them.

Column {data-width=1}
---

### **References**

1. Bates, N. A., Ford, K. R., Myer, G. D., & Hewett, T. E. (2013). Impact differences in ground reaction force and center of mass between the first and second landing phases of a drop vertical jump and their implications for injury risk assessment. Journal of Biomechanics, 46(7), 1237–1241. https://doi.org/10.1016/j.jbiomech.2013.02.024

2. Hewett, T. E., Ford, K. R., Hoogenboom, B. J., & Myer, G. D. (2010). Understanding and preventing acl injuries: Current biomechanical and epidemiologic considerations - update 2010. North American Journal of Sports Physical Therapy: NAJSPT, 5(4), 234–251.

3. Hewett, T. E., Myer, G. D., Ford, K. R., Heidt, R. S., Colosimo, A. J., McLean, S. G., van den Bogert, A. J., Paterno, M. V., & Succop, P. (2005). Biomechanical measures of neuromuscular control and valgus loading of the knee predict anterior cruciate ligament injury risk in female athletes: A prospective study. The American Journal of Sports Medicine, 33(4), 492–501. https://doi.org/10.1177/0363546504269591

4. Kabacinski, J., Murawa, M., Dworak, L. B., Maczynski, J. (2017). Differences in ground reaction forces during landing between volleyball spikes. Trends in Sports Science, 2(24), 87-92. 

5. Paterno, M. V., Ford, K. R., Myer, G. D., Heyl, R., & Hewett, T. E. (2007). Limb asymmetries in landing and jumping 2 years following anterior cruciate ligament reconstruction. Clinical Journal of Sport Medicine: Official Journal of the Canadian Academy of Sport Medicine, 17(4), 258–262. https://doi.org/10.1097/JSM.0b013e31804c77ea

6. Young, W. K., Briner, W., & Dines, D. M. (2023). Epidemiology of Common Injuries in the Volleyball Athlete. Current Reviews in Musculoskeletal Medicine, 16(6), 229–234. https://doi.org/10.1007/s12178-023-09826-2

7. Yu, B., & Garrett, W. E. (2007). Mechanisms of non-contact ACL injuries. British Journal of Sports Medicine, 41 Suppl 1(Suppl 1), i47-51. https://doi.org/10.1136/bjsm.2007.037192

Bio
===

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### **Who Am I?**

My name is **Noah Clemens**.

I'm currently a **Sophomore** at the University of Dayton. I anticipate graduating in May **2026**, with my BS in **Health Science**, a concentration in **Exercise and Movement Science**, a minor in **Data Analytics**, and I'm planning on adding an additional minor in **Human Movement Biomechanics**.

I'm a member of the **Men's Cross Country** team, part of the UD **Honors Program**, Treasurer of the **Math Club**, and member of the **Kinesiology Research Group**.

I plan on attending some form of graduate school following my undergraduate studies. At this point, I'm interested in either becoming a **Physical Therapist**, or obtaining a graduate degree in **Kinesiology**, **Biomechanics**, **Physiology**, or a similar field.

Column {data-width=1}
---

###

```{r}
knitr::include_graphics("~/Desktop/MTH209/Labs/NClemHeadshot.jpeg", error=F)
```

###

```{r}
knitr::include_graphics("~/Desktop/MTH209/Labs/NClemRunning.jpeg", error=F)
```